Interdisciplinary Computational Science – Chemical and Biological Physics: Advanced Course for Science High School Students

Leading team

Project members

 

Team members – Past

Summary

In the world of contemporary research and technology there is a growing interest for multidisciplinary fields. In particular, simplified models grounded in physical concepts and principles serve to explain structure formation phenomena in multi-particle systems that are at the center of interest in chemistry, biology and material engineering. However, current high–school science curriculum does not engage students in simplifying complex phenomena, nor does it equip them with the conceptual framework or numerical tools required to analyze such phenomena.

Interdisciplinary Computational Science (ICS) – Chemical and Biological Physics is a novel school subject intended to respond to these challenges. It comprises of a three-year learning sequence (10th to 12th grade) oriented toward capable and motivated high school science students who are interested in interdisciplinary, project-based learning. The conceptual framework comprises of computational modeling (e.g. molecular dynamics and random walk models for the time evolution of multi-particle systems) and analytical tools (foundations of statistical mechanics to describe system behavior in equilibrium) providing students with tools for analyzing self-organization and structure formation phenomena that are of interest in current interdisciplinary science.

The ICS learning sequence incorporates novel characteristics, assisting in reducing cognitive load as well as overcoming conceptualization gaps and inconsistencies appearing in traditional introductory / thermal physics curricula:

– Newtonian based dynamical models of multi-particles systems precedes statistical modelling. A coarse graining spatial and temporal process helps clarifying the transition from deterministic to stochastic behavior in such systems.

–  Analysis of energy dispersion phenomenon (thermal contact) is carried out in analogy to spatial distribution of particles in diffusion, based on entropy and the 2nd law of thermodynamics.

– A new ordering of the statistical mechanics sequence separates between kinetic and potential energy, corresponding to their non-dependence in multi-particle systems. Kinetic energy is discussed with respect to thermal contact, while potential energy is presented later in context of structure formation, for finite-range interacting particles.

The research-based development of the ICS program was based on a pilot program titled Soft Matter. It was carried out within the MER (Educational Reconstruction) framework, consisting of three iterative dimensions:

  1. Analysis of content structure: clarifying the scientific subject matter for the program, selection of central ideas and their organization into a coherent knowledge structure.
  2. The construction of instruction: consisting of the implementation of pedagogical principles to design a learning environment given the constraints of implementation.
  3. The empirical investigations: including the study of students’ conceptions, practices, achievements and learning processes in the program context.

The MER dimensions were applied in 3 consecutive development cycles, each modified by the empirical studies of its previous implementations. The first development cycle was based on the research findings in a pilot program titled “Soft and Messy Matter.”

Research on students’ perception and comprehension with respect to both statistical mechanics content matter and modelling practices consisted of the following studies:

  1. Examining students’ perceptions regarding modelling practices; how they distinguish between scientific principles, the computational procedure, and the epistemological conceptualization (e.g., simplification assumptions) in context of two and multi-particle systems (Haim Edri thesis, supervisors: Prof. Edit Yerushalmi, Prof. Bat-Sheva Eylon).
  2. Studying students’ understanding of statistical mechanics concepts and principles in context of spatial distributions, as well as their preferences of statistical vs. dynamical reasoning (Ariel Steiner thesis, supervisors: Prof. Edit Yerushalmi, Prof. Samuel Safran).
  3. Investigating students’ invoking and application of the statistical mechanics framework, as well as their knowledge organization coherence, alternative reasoning patterns and preference of dynamical vs. statistical modelling. This was carried out in contexts of energy dispersion (thermal contact), and structure formation (adsorption) phenomena (Ariel Abrashkin thesis, supervisors: prof. Edit Yerushalmi, prof. Samuel Safran)

 

Administration

Rina Kimchi

Links for further reading

  •  Abrashkin A. (2021). Statistical thermodynamics – Research-Based Development of a Curricular Unit in an Interdisciplinary Computational Science Program. (Unpublished doctoral dissertation). Rehovot, Israel: The Weizmann Institute of Science
  • Langbeheim, E., Abrashkin, A., Steiner, A., Edri, H., Safran, S., & Yerushalmi, E. (2020). Shifting the learning gears: redesigning a project-based course on soft matter through the perspective of constructionism. Phys Rev – PER, 16(2), 020147.
  • Langbeheim, E. (2020). Simulating the Effects of Excluded-Volume Interactions in Polymer Solutions. Journal of Chemical Education, 97(6), 1613-1619.‏ https://doi.org/10.1021/acs.jchemed.0c00003
  • Edri H. (2019). Bringing Simplification Assumptions to the Forefront in Chemical and Biological Physics: Research-Based Development of an Introductory Computational Science Curriculum. (Unpublished doctoral dissertation). Rehovot, Israel: The Weizmann Institute of Science.
  • Steiner A. (2019). Research based design of an instroctural unit Statistical mechanics – model of the diffusion phenomenon. (Unpublished doctoral dissertation). Rehovot, Israel: The Weizmann Institute of Science.
  • Langbeheim, E., Edri, H., Schulmann, N., Safran, S., & Yerushalmi, E. (2019). Extending the Boundaries of High-School Physics: Introducing Computational Modeling of Complex Systems. In Sunal C. S., Sunal D. W., Harrell J. W. & Shemwell J. T. (Eds.). Physics Teaching and Learning. (pp. 111-134) Charlotte, NY.
  • Langbeheim, E., Safran, S. A. & Yerushalmi, E. (2016). Engagement in theoretical modelling in research apprenticeships for capable high school students. In Taber, K. S., & Sumida, M. (Eds.). International Perspectives on Science Education for the Gifted: Key issues and challenges. (pp. 61-74). Routledge.
  • Langbeheim, E., Safran, S. A., & Yerushalmi, E. (2014), Visualizing the Entropy Change of a Thermal Reservoir, J. Chem. Educ., 91 (3), pp 380–385; DOI: 10.1021/ed400180w
  • Yerushalmi, E., (2013), Editorial: The challenge of teaching soft matter at the introductory level, Soft matter, 9, (pp 5316-5318), RSC publication, DOI: 10.1039/C3SM90028B
  • Langbeheim, E., Livne, S., Safran, S. A., & Yerushalmi, E., (2013), Evolution in students’ understanding of thermal physics with increasing complexity, Phys Rev – ST PER ,9, 020117, DOI:10.1103/PhysRevSTPER.9.020117.
  • Langbeheim, E., Livne, S., Safran, S. A., & Yerushalmi, E. (2012) Introductory physics going soft, Am. J. Phys. 80, 51-60, doi:10.1119/1.3647995.

.לנגבהיים א. ליבנה ש. שפרן ש. ירושלמי ע. מגן א. (2012), התארגנות עצמית בחומרים רכים,  על-כימיה

 

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